Ancient Yi Script Handwriting Sample Repository.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2024-10-30 DOI:10.1038/s41597-024-03918-5

Xiaojuan Liu, Xu Han, Shanxiong Chen, Weijia Dai, Qiuyue Ruan

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引用次数: 0

Abstract

The ancient Yi script has been used for over 8000 years, which can be ranked with Oracle,Sumerian,Egyptian,Mayan and Harappan,and is one of the six ancient scripts in the world. In this article, we collected 2922 handwritten single word samples of commonly used ancient Yi characters. Each character was written by 310 people respectively, with a total of 427,939 valid characters. We completed continuous handwritten text sampling, written by 250 people, with 5 texts per person, covering topics such as Yi astronomy, geography, rituals, and agriculture. In the process of data collection, we proposed an automatic sampling method for ancient Yi script, and completed the automatic cutting and labeling of handwritten samples. Furthermore, we tested the recognition performance of the sorted data set under different deep learning network models. The results show that ancient Yi script has diverse shape structures and rich writing styles, which can be used as a benchmark data set in related fields such as handwritten text recognition and handwritten text generation.

查看原文本刊更多论文

古彝文手写样本库。

古彝文已有 8000 多年的历史，与甲骨文、苏美尔文、埃及文、玛雅文、哈拉帕文齐名，是世界六大古文字之一。本文收集了 2922 个常用古彝文手写单字样本。每个字分别由 310 人书写，共计 427 939 个有效字。我们完成了由 250 人书写的连续手写文本采样，每人 5 篇，内容涉及彝族天文、地理、礼仪、农业等。在数据采集过程中，我们提出了彝文古文字自动采样方法，并完成了手写样本的自动切割和标注。此外，我们还测试了分类数据集在不同深度学习网络模型下的识别性能。结果表明，古彝文具有多样的形状结构和丰富的书写风格，可以作为手写文字识别和手写文字生成等相关领域的基准数据集。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.